Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language
It addresses the challenge of hierarchical task execution for agents using natural language, which is incremental over prior flat-sequence approaches.
The paper tackles the problem of natural language command of situated agents by introducing a hierarchical formalism of procedures as programs, outperforming reactive baselines on IQA and ALFRED datasets with a large margin.
When humans conceive how to perform a particular task, they do so hierarchically: splitting higher-level tasks into smaller sub-tasks. However, in the literature on natural language (NL) command of situated agents, most works have treated the procedures to be executed as flat sequences of simple actions, or any hierarchies of procedures have been shallow at best. In this paper, we propose a formalism of procedures as programs, a powerful yet intuitive method of representing hierarchical procedural knowledge for agent command and control. We further propose a modeling paradigm of hierarchical modular networks, which consist of a planner and reactors that convert NL intents to predictions of executable programs and probe the environment for information necessary to complete the program execution. We instantiate this framework on the IQA and ALFRED datasets for NL instruction following. Our model outperforms reactive baselines by a large margin on both datasets. We also demonstrate that our framework is more data-efficient, and that it allows for fast iterative development.